Lesson 11 – How to Read and Write CSV Files in Python: A Beginner’s Guide

🔹 Introduction

In this tutorial, you'll learn step-by-step how to:

• Understand what a CSV file is
  
  

• Read data from a CSV file using both csv and pandas
  
  

• Write data to a CSV file
  
  

• Avoid common mistakes beginners make
  
  

Let’s dive in!

🔹 What Is a CSV File?

A CSV file is a plain text file that stores tabular data—like a spreadsheet—where each row is a line and each value is separated by a comma.

Example of a CSV file:

name,age,city

Alice,25,New York

Bob,30,London

Carol,22,São Paulo

CSV files are lightweight, easy to generate, and compatible with almost any spreadsheet software like Excel or Google Sheets.

📖 Reading a CSV File Using Python’s Built-in csv Module

Python includes a built-in module called csv that allows you to read CSV files easily.

Example:

import csv

with open('data.csv', newline='') as file:

reader = csv.reader(file)

for row in reader:

print(row)

Output:

['name', 'age', 'city']

['Alice', '25', 'New York']

['Bob', '30', 'London']

['Carol', '22', 'São Paulo']

💡 Tip: Use newline='' to prevent extra blank lines when reading files on Windows.

✏️ Writing to a CSV File with csv.writer

Writing to a CSV file is just as simple:

import csv

data = [

['name', 'age', 'city'],

['Alice', 25, 'New York'],

['Bob', 30, 'London'],

['Carol', 22, 'São Paulo']

]

with open('data.csv', mode='w', newline='') as file:

writer = csv.writer(file)

writer.writerows(data)

This will create (or overwrite) a file called data.csv with the content above.

🐼 Reading CSV Files with pandas

While csv is great for small tasks, pandas is the go-to library for working with larger or more complex datasets.

import pandas as pd

df = pd.read_csv('data.csv')

print(df)

Output:

pgsql

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name age city

0 Alice 25 New York

1 Bob 30 London

2 Carol 22 São Paulo

You can also access specific columns or rows easily:

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print(df['name']) # prints the "name" column

print(df.iloc[0]) # prints the first row

🚫 Common Mistakes to Avoid

• Forgetting newline='' on Windows: This can lead to blank lines between rows.
  
  

• Not using mode='w' or mode='a': Always specify the mode when opening a file for writing.
  
  

• Not handling headers: Remember to write headers manually with the csv module.
  
  

• Not using try-except blocks: Always handle file-related errors gracefully.

📌 Summary

By now, you should feel confident in:

• Opening and reading a CSV file with both csv and pandas
  
  

• Writing your own CSV files from Python data structures
  
  

• Avoiding common beginner pitfalls
  
  

CSV files are often the first step in learning how to process real-world data. Once you're comfortable here, you're ready to move on to topics like data visualization, data cleaning, and working with APIs.